Healthcare Robotics Jobs Mexico City

Healthcare robotics roles on Rex.zone focus on building, integrating, and validating robotic systems used in clinical workflows, hospital operations, and patient-facing care. You will work in remote full-time engineering teams to develop robotic perception, navigation, controls, and safety validation that improve real-world outcomes. This job page targets Healthcare Robotics Jobs Mexico City while supporting remote US-based employment through Rex.zone, connecting candidates with robotics software engineering, ML-assisted perception, QA evaluation, and compliance-driven development for healthcare environments.

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Job Heading: Healthcare Robotics Engineer (Mexico City)

Title: Healthcare Robotics Engineer (Mexico City) Date: 25-02-2026 Company: Rexzone Country: US Remote Type: Remote Employment Type: FULL_TIME Experience Level: Mid-Senior Industry: Technology Job Function: Engineering Skills: healthcare robotics, ROS2, robot perception, motion planning, SLAM, computer vision, safety validation, system integration Salary Currency: USD Salary Min: 63360 Salary Max: 126720 Pay Period: YEAR You will design and ship software for healthcare robotics systems, including perception, localization, mapping, planning, and closed-loop control. The work emphasizes safety, reliability, and traceable verification for hospital-like environments, with strong system integration across sensors, embedded hardware, and cloud services. Responsibilities: - Build and maintain ROS2-based robotics software stacks for healthcare workflows - Develop perception modules (computer vision, sensor fusion) for detection and tracking in clinical environments - Implement SLAM and navigation behaviors suited to dynamic indoor spaces - Improve motion planning, trajectory optimization, and collision avoidance around people and equipment - Define validation plans, test protocols, and safety verification for deployment readiness - Collaborate with cross-functional teams on requirements, integration, and post-deployment monitoring Preferred Qualifications: - Experience shipping robotics systems to production or regulated environments - Knowledge of functional safety concepts, risk analysis, and verification/validation practices - Familiarity with simulation (Gazebo/Ignition), hardware-in-the-loop testing, and CI for robotics - Strong fundamentals in controls, estimation, and real-time system debugging How Rex.zone Helps: - Centralized application flow and role matching for robotics engineering teams - Remote-first collaboration patterns, documentation standards, and structured feedback loops - Access to related roles in ML-assisted perception, computer vision annotation, and QA evaluation

What You Will Work On

Core workflows commonly include: - Robot perception: camera/LiDAR processing, object detection, semantic mapping, human-aware tracking - Autonomy: SLAM, navigation, task planning, recovery behaviors, fleet coordination - Controls: tuning controllers, latency reduction, robustness under sensor noise and occlusions - Integration: ROS2 nodes, sensor drivers, edge compute, cloud telemetry, OTA updates - Safety and QA: test plans, regression suites, incident triage, performance monitoring - Data and evaluation: labeling edge-case data, prompt evaluation for operator tooling, QA evaluation of autonomy logs

Related AI/ML and Data Work That Supports Robotics

Many healthcare robotics teams also rely on AI/ML training pipelines. Depending on project needs, you may collaborate with specialists for: - Data labeling for robot perception datasets (bounding boxes, segmentation, keypoints) - Computer vision annotation and named entity recognition for clinical workflow data - QA evaluation and content safety labeling for operator-facing tools - LLM training pipelines for documentation assistants and robotics support agents - RLHF-style feedback loops for improving human-robot interaction prompts and responses These workflows improve training data quality, annotation guidelines compliance, and model performance improvement across robotics perception and operator tooling.

Work Model, Modifiers, and Role Variants

This page targets remote, full-time Healthcare Robotics Jobs Mexico City. Additional modifiers candidates often explore on Rex.zone include: - Contract, freelance, and part-time robotics roles - Entry-level and senior robotics engineering positions - NLP, computer vision, and LLM evaluation roles supporting robotics teams - Employer types: AI labs, tech startups, healthcare technology companies, annotation vendors, and BPO partners

How to Apply

Apply through Rex.zone with a resume highlighting robotics system delivery, ROS2 projects, perception/navigation experience, and evidence of safety-minded engineering. Include links to repositories, technical write-ups, or demos where possible.

Frequently Asked Questions

  • Q: Are these Healthcare Robotics Jobs Mexico City roles remote?

    Yes. The roles on this page are explicitly marked Remote and are listed under Rex.zone for remote-first collaboration.

  • Q: Why does the job title include Mexico City if the country is US?

    The keyword targets Healthcare Robotics Jobs Mexico City for search intent, while the role metadata keeps the default Country as US and the work arrangement as Remote per the posting requirements.

  • Q: What skills are most important for a Healthcare Robotics Engineer?

    Common skills include ROS2, robot perception, SLAM, motion planning, computer vision, safety validation, and system integration, with strong debugging and testing practices.

  • Q: Do healthcare robotics teams use AI data labeling and evaluation workflows?

    Often, yes. Robotics perception models benefit from data labeling, computer vision annotation, training data quality checks, and QA evaluation. Some teams also use RLHF-style feedback for human-robot interaction tooling.

  • Q: What does success look like in the first 90 days?

    Typical outcomes include shipping improvements to a ROS2 stack, increasing navigation reliability, adding regression tests, validating perception performance on new data, and contributing to safety and deployment readiness.

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